COVID‐19 as a Blood Clotting Disorder Masquerading as a Respiratory Illness: A Cerebrovascular Perspective and Therapeutic Implications for Stroke Thrombectomy
Bibliographic record
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the name suggests was initially thought to only cause a respiratory illness. However, several reports have been published of patients with ischemic strokes in the setting of coronavirus disease 2019 (COVID-19). The mechanisms of how SARS-CoV-2 results in blood clots and large vessel strokes need to be defined as it has therapeutic implications. SARS-CoV-2 enters the blood stream by breaching the blood-air barrier via the lung capillary adjacent to the alveolus, and then attaches to the angiotensin-converting enzyme II receptors on the endothelial cells. Once SARS-CoV-2 enters the blood stream, a cascade of events (Steps 1-8) unfolds including accumulation of angiotensin II, reactive oxygen species, endothelial dysfunction, oxidation of beta 2 glycoprotein 1, formation of antiphospholipid antibody complexes promoting platelet aggregation, coagulation cascade, and formation of cross-linked fibrin blood clots, leading to pulmonary emboli (PE) and large vessel strokes seen on angiographic imaging studies. There is emerging evidence for COVID-19 being a blood clotting disorder and SARS-CoV-2 using the respiratory route to enter the blood stream. As the blood-air barrier is breached, varying degrees of collateral damage occur. Although antiviral and immune therapies are studied, the role of blood thinners in the prevention, and management of blood clots in Covid-19 need evaluation. In addition to ventilators and blood thinners, continuous aspiration and clot retrieval devices (approved in Europe, cleared in the United States) or cyclical aspiration devices (approved in Europe) need to be considered for the emergent management of life-threatening clots including PE and large vessel strokes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".